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4,633 results for “currents”
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>three of the six paths of the northern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>the six paths of the southern HOME tomography array.</p>
A high-resolution, multi-decadal, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 3.0, 1994-2019)
<p>The data is from a Regional Ocean Modelling System free-running, hydrodynamic simulation of the East Australian Current System. The model has a horizontal resolution of 2.5-6 km in the cross-shore direction and 5 km in the alongshore direction, and 30 vertical s-levels. The model domain covers the southeastern Australia oceanic region from 25.1-41.5°S and 147.1-162.2°E, and the grid is orientated 20 degrees clockwise to be predominantly orientated alongshore. The time period covered is 02 Jan 1994 to 28 Feb 2019. The model outputs provided are daily averages of the following variables: Two-dimensional variables: Sea surface height (zeta), barotropic cross-grid velocity (u) and barotropic along-grid velocity (v). Three-dimensional variables: Temperature (temp), salinity (salt), density (rho), cross-grid velocity (u), along-grid velocity (v) and vertical velocity (w), temperature time rate of change (temp_rate), temperature horizontal advection term (temp_hadv), temperature vertical advection term (temp_vadv), temperature horizontal diffusion term (temp_hdiff), temperature vertical diffusion term (temp_vdiff). In this version, the heat budget terms (temp_rate, temp_hadv, temp_vadv, temp_hdiff and temp_vdiff) are set to be zeros on the land.</p> <p> </p> <p>This model is part of the <a href="../records/8294716"><strong>South East Australian Coastal Ocean Forecast System (SEA-COFS)</strong></a> suite of models.</p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Dataset of "Black Titanium Oxide/Activated TaS2 Flakes Photoelectrode for Plasmon Assisted Hydrogen Evolution at Neutral pH at High Current Density"
<p>Nanotubular structure of black titania with sputtered gold and incorporation of 3R-TaS2 self-activated flakes for high current density and neutral pH usage for hydrogen evolution reaction. Dataset consists of electrochemical data (LSV, EIS, CA), x-ray difractograms, Raman spectra, SEM images with EDX mapping, UV-vis spectra, DEMS records, ICP-MS records, XPS spectra and compositional analysis and BET records.</p>
Data in support of 'The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo'
<p>Data in support of 'Chandler M, Zilberman NV, Sprintall J. (2024). The deep western boundary current of the Southwest Pacific Basin: insights from Deep Argo. <em>Journal of Geophysical Research: Oceans</em>. <a href="https://doi.org/10.1029/2024JC021098" target="_blank" rel="noopener">https://doi.org/10.1029/2024JC021098</a>'</p> <p>There are 4 netCDF files:</p> <ol> <li>swpb_dwbc_deep_argo_profiles_chandler2024.nc</li> <li>swpb_dwbc_deep_argo_trajectories_chandler2024.nc</li> <li>kt_dwbc_deep_argo_time_series_chandler2024.nc</li> <li>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</li> </ol> <p><strong>swpb_dwbc_deep_argo_profiles_chandler2024.nc </strong>contains the delayed-mode profiles of potential temperature and salinity on a 10-dbar pressure grid from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[pressure; latitude; longitude; time; wmo_id; theta; salinity]</em></p> <p><strong>swpb_dwbc_deep_argo_trajectories_chandler2024.nc </strong>contains delayed-mode trajectories from the Deep Argo floats profiling within the deep western boundary current of the Southwest Pacific Basin. <em>[latitude; longitude; u; v; pressure; wmo_id; time]</em></p> <p><strong>kt_dwbc_deep_argo_time_series_chandler2024.nc</strong> contains the 2021--2022 monthly time series of dynamic height, salinity, and potential temperature between 2000--4000-dbar computed from the spatially-averaged Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[time; pressure; theta; salinity; dh; region_long; region_lat]</em></p> <p><strong>kt_dwbc_deep_argo_seasonal_cycles_chandler2024.nc</strong> contains seasonal cycles of dynamic height, salinity, and potential temperature (including the decomposition into heave/spice) between 2000--4000-dbar from the Deep Argo profiles within the deep western boundary current as it travels along the western side of the Kermadec Trench. <em>[pressure; theta; theta_heave; theta_spice; salinity; dh; region_long; region_lat]</em></p> <p>Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (<a href="https://argo.ucsd.edu/" target="_blank" rel="noopener">https://argo.ucsd.edu/</a>). The Argo Program is part of the Global Ocean Observing System. A full list of acknowledgements can be found in the affiliated <a href="https://doi.org/10.1029/2024JC021098">publication</a>.</p> <p><code>Version history:</code><br><code>v1.0 First created (06-March-2024)</code><br><code>v1.1 Updated to include accepted publication reference (15-October-2024)</code></p>
Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power
<p>This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.</p>
Moored current and temperature measurements in the Southern Adriatic Sea at mooring site BB and FF, March 2012-June 2020
<p>This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea). </p>
Data from: Will Current Protected Areas Harbour Refugia for Threatened Arctic Vegetation Types until 2050? A First Assessment
<p>We present predictions of Arctic vegetation for 2050 based on a combination of climate models (namely, EC-Earth3-Veg, IPSL-CM6A-LR, and MRI-ESM2-0), emission scenarios (names, SSP126 and SSP585) and tree dispersal rate scenarios (unrestricted, 20km and 5km) based on the methods of Pearson et al. (2013) and the new raster version of the Circumpolar Arctic Vegetation Map (CAVM) (Raynolds et al. 2019). We additionally present a dataset summarising total areas for each vegetation type in the CAVM and the forecasted models based on the computation of zonal histograms in ArcGIS (zonal_histogram_results.csv), for the total Arctic as well as only within protected areas, defined by the Map of Arctic Protected Areas (CAFF and PAME 2017). We also present a potential map of refugia for what we deem the realistic model (IPSL, SSP585, 20 km tree dispersal) as a raster file. Refugia were identified as regions where the vegetation remained the same between the CAVM and the predictions. Additionally, we present a map of model agreement, showing the degree to which other models agree with the vegetation classification for our refugia.</p> <p>All predictions named according to the tree dispersal rate, climate model, and emissions scenario, preceded by the term "pred". For example: "pred_unres_mri_585" represents the unrestricted tree dispersal, MRI-ESM-0 climate model, and SSP585 scenario-based prediction. The MRI-ESM-0 x SSP585 combination had gaps in data which results in a lack of predictions in some areas; this affects 3 models.</p> <p>Further details and all code associated with these datasets are found <a href="https://github.com/PlekhanovaElena/Arctic_vegetation_prediction">here</a>.</p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
City of Seattle, Seattle Public Utilities, Annual Bull Trout Redd Surveys in Tributaries to Chester Morse Lake 1996-current, Cedar River Municipal Watershed, King County, WA
These data were collected during weekly annual redd surveys conducted by Seattle Public Utilities (SPU) in the Cedar River Municipal Watershed (CRMW), 1996 - current. Annual weekly bull trout redd surveys funded through the CRMW Habitat Conservation Plan (HCP) began in 2000 and ended in 2011 spawning year. To reinstate a monitoring program for the population, redd surveys in the most heavily used habitats by bull trout (termed the Core Zone), were opportunistically conducted in 2018. Weekly annual surveys in most of the Core Zone were reinstated in 2019. Approximately 77% of all redds observed 2000 - 2011 would have been observed during those years using the 2019 - 2022 spatial survey extent (SPU data on file). In 2023, the spatial and temporal coverage of surveys were on par with historical coverage, i.e., approximately 100% of all redds observed 2000 - 2011 would have been observed using the 2023 spatial survey extent. Information on redd location is used primarily to enable derivation of redd elevations. Redd elevation is required to estimate potential impacts to the spawning population and incubating embryos caused by reservoir inundation of stream spawning habitat after the spawning period during fall through spring. Redd weekly timing information is critical to accurately represent whether embryos remain in the gravel and are vulnerable to impacts of reservoir inundation as the reservoir is refilled starting in early spring. It is also vitally important that SPU understand timing and abundance of redds beyond the inundation zone to enable understanding of the overall impact to the population.
Missouri reservoir water quality data (2022 - current) from the Statewide Lake Assessment Program (SLAP)
This dataset of limnological water quality data continues from North et al., 2025, starting in 2022 until present. The data is from reservoirs, primarily within the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: ammonium (NH4), anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, dissolved organic carbon, microcystin, nitrate & nitrite (NO3), pheophytin, particulate inorganic matter, particulate organic matter, phycocyanin, saxitoxin, Secchi disk depth, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids, and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete depths in the hypolimnion.
North Temperate Lakes LTER: Pelagic Macroinvertebrate Summary 1983 - current (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/14/32. The abstract below was extracted from the Level 0 data package and is included for context: This is a summary of dataset NTL 13. Derived data include the mean and standard deviation of the number of each species captured as well as the mean and standard deviation of the density of individuals on both an areal and volumetric basis. Five vertical tows are done at the deepest point of each of the seven primary lakes in the Trout Lake area (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes and bog lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog]) using a 1-mm mesh net with a 1-m wide mouth. On Trout Lake four additional sites are sampled, where depths are approximately at 10 m, 15 m, 20 m, and 25 m respectively, with three tows done at each site. Trout Lake was the only lake sampled in 2020. All samples are taken in darkness. Samples are preserved and counted, yielding numbers caught. These night tows target the large invertebrate planktivore component of the pelagic zooplankton community. Sampling Frequency: annually Number of sites: 11
North Temperate Lakes LTER Pelagic Macroinvertebrate Abundance 1983 - current (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/13/32. The abstract below was extracted from the Level 0 data package and is included for context: Five vertical tows are done at the deepest point of each of the seven primary lakes in the Trout Lake area (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes and bog lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog]) using a 1-mm mesh net with a 1-m wide mouth. On Trout Lake four additional sites are sampled, where depths are approximately at 10 m, 15 m, 20 m, and 25 m respectively, with three tows done at each site. Trout Lake was the only lake sampled in 2020. All samples are taken in darkness. Samples are preserved and counted, yielding numbers caught. These night tows target the large invertebrate planktivore component of the pelagic zooplankton community. Sampling Frequency: annually Number of sites: 11
Chlorophyll and phaeopigments measured from discrete bottle samples from CCE LTER process cruises in the California Current System, determined by extraction and bench fluorometry, 2006 - 2024 (ongoing).
Discrete bottle samples taken from various depths in the CCE region are filtered (known volumes) onto GF/F filters onboard the CCE Process cruises (since 2006, ongoing). The filters are placed into culture tubes containing 90% acetone, and the fluorescence of the samples is read on a fluorometer after 24 to 48 hours. The samples are then acidified to degrade the chlorophyll to phaeopigments (non-photosynthetic pigments) and a second reading is taken. The readings prior to and after acidification are used to calculate concentrations of both chlorophyll a and phaeopigments (i.e. phaeophytin).
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
Temporal and spatial changes of the abundance and species composition of phytoplankton in the California Current from samples collected aboard CalCOFI cruises from summer 1996 through 2022.
The abundances of 385 taxonomic categories of phytoplankton (species where possible) are presented for the 26.5 -year period beginning with summer, 1996 and concluding with autumn 2022. There were four cruises per year. Samples were water samples collected from the second depth, which was designed to sample the mixed layer when a mixed layer existed, generally between 5m - 15m. Before counting, samples from single stations were pooled into four regions: NE (northern inshore), SE (southern inshore), Alley (the region of the California Current) and Offshore (Central Pacific). Pooled samples were enumerated with an inverted microscope. The species data are presented by seven major taxonomic categories followed by the sums of those major taxa. The species codes are defined in the table metadata.
Dissolved trace element concentration profiles of micronutrients (Mn, Ni, Cu, Zn, Co) and contaminants (Cd, Pb) in seawater from discrete bottle samples from CCE Process Cruises in the California Current System, 2021 - 2025 (ongoing).
Dissolved trace element is sampled from the trace metal clean rosette. The sample is collected by filtering seawater through a 0.2µm PES filter. The seawater sample is then acidified to pH~1.8 using ultra clean hydrochloric acid and subsequently analyzed using sector-field inductively coupled plasma-mass spectrometry, scanning in low and medium resolution, with either standard curve or isotope dilution methods. The samples are used to develop a description of the distribution of dissolved trace elements in the CCE region.
North Temperate Lakes LTER: Chemical Limnology of Primary Study Lakes: Nutrients, pH and Carbon 1981 - current
Parameters characterizing the nutrient chemistry of the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra, and Fish) are measured at multiple depths throughout the year. These parameters include total nitrogen, total dissolved nitrogen, nitrite+nitrate-N, ammonium-N, total phosphorus, total dissolved phosphorus, dissolved reactive phosphorus (only in the southern lakes and not in Wingra and Fish after 2003), bicarbonate-reactive filtered and unfiltered silica (both discontinued in 2003), dissolved reactive silica, pH, air equilibrated pH (discontinued in 2014 in the northern lakes and in 2020 in the southern lakes), total alkalinity, total inorganic carbon, dissolved inorganic carbon, total organic carbon, dissolved organic carbon, and total particulate matter (only in the northern lakes in this data set; total particulate matter in southern lakes starting in 2000 is available in a separate dataset). Sampling Frequency: Northern lakes- monthly during ice-free season - every 5 weeks during ice-covered season. Southern lakes- Southern lakes samples are collected every 2-4 weeks during the summer stratified period, at least monthly during the fall, and typically only once during the winter, depending on ice conditions. Number of sites: 11
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.